The aim of this study was to propose and crossvalidate an anthropometric model for the simultaneous estimation of fat mass (FM), bone mineral content (BMC), and lean soft tissue (LST) using DXA as the reference method. A total of 408 boys (8–18 years) were included in this sample. Wholebody FM, BMC, and LST were measured by DXA and considered as dependent variables. Independent variables included thirtytwo anthropometrics measurements and maturity offset determined by the Mirwald equation. From a multivariate regression model
Estimate body composition of children is not an easy task, since the relationships between body components during growth are not constant as in adults. Anthropometricbased equations remain an adequate alternative for determining the body composition of pediatric populations in field settings. However, the advent of new technologies has enabled new ways for body composition assessment, thus, rendering the traditional anthropometry inaccuracy as a representative standard [
The advent of dualenergy Xray absorptiometry (DXA), measures of FM, bone mineral content (BMC), and lean soft tissue (LST) are obtained. Hence, DXA can be considered as a 3C model since the estimates of three components are obtained as follows: first by separating pixels into those with soft tissue only (FM plus LST) and those with soft tissue plus BMC, based on two different photon energies (lower and higher energies, resp.) [
However, the availability of DXA in the clinical and fields settings is limited given its cost. Therefore, simple solutions are required for estimating body composition in children and anthropometric parameters, such as skinfolds and circumferences, which have been widely used as bedside techniques in different contexts. Thus, the aim of this study was to develop and crossvalidate multicomponentanthropometricbased equations to simultaneously estimate FM, BMC, and LST in a male pediatric population, using DXA as the criterion method.
The study followed a crosssectional design, consisting of a sample of 408 young males between 8 and 18 years of age. The subjects were recruited voluntarily from a population of students that could be engaged in systematic programs of sports, or not, considered as athletes and nonathletes, respectively. The athletes came from sports centers (
The study followed the guidelines and regulations of directing human research, and agreements were obtained from the parents or guardians to all procedures. The approval was granted by the Ethics in Research Department of the School of Physical Education and Sport, University of São Paulo (CEP332007/EEFE/04.04.20072006/32), which also adhere to the Helsinki Declaration.
Each subject was evaluated in the laboratory, in the morning after an overnight fast, in a single session, and always by the same examiner, and all measurements, were performed during a period of three months. Before the measurements the subjects were asked to empty their bladders. Dressed in shorts and shirt, the totalbody DXA examination was applied using the system for totalbody scan, according to manufacturer’s guidelines. The anthropometric measures were performed according to the literature recommendations [
Whole and regional body composition was estimated with a DXA Scanner Lunar DPXNT (GE Medical, Software Lunar DPX enCORE 2007 version 11.40.004, Madison, WI). The software identified the physical characteristics of ethnicity, gender, and age and automatically adjusted the scan mode, speed, and images resolution.
Body weight was determined from DXA, and the dependent variables of interest were fat mass (FM, kg), bone mineral content (BMC, kg), and lean soft tissue (LST, kg).
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To ensure the precision of the results, intra evaluator technical errors of measurement absolute (TEM) and relative (TEM%) were calculated (Table
Descriptive statistics of body composition in boys (
Range  Mean  SD  TEM  TEM%  CI 95%  

DXA  
Fat mass (kg)  1.3–41.8  9.3  7.5  0.22  1.42  8.6–10.0 
Bone mineral content (kg)  0.7–4.1  2.1  0.8  0.01  0.03  2.1–2.2 
Lean mass tissue (kg)  17.1–72.6  38.1  12.7  0.06  0.15  36.9–39.4 
Age/maturation/anthropometrics  
Age (year)  8–18  13.7  2.99  —  —  12.9–13.5 
PHV (year)  −4.7–4.5  −0.5  2.5  —  —  −0.8–0.3 
Seating height (cm)  61.5–99.5  82.3  8.8  0.26  0.30  81.4–83.1 
Height (cm)  120.3–196.8  158.1  17.7  0.17  0.11  156.4–159.8 
Weight (kg)  20.6–119.4  50.2  17.4  0.27  0.29  48.5–51.9 
Suprailiac skinfold (mm)  2.8–64.5  13.3  10.2  0.35  2.27  12.4–14.3 
Horizontal abdominal skinfold (mm)  1.5–66.0  16.5  12.0  1.59  4.96  15.4–17.7 
TEM: absolute technical error of measurement; TEM%: relative technical error of measurement; CI: confidence interval; DXA: dualenergy Xray absorptiometry; PHV: years for peak height velocity.
The SPSS Statistics, version 13, for Windows (SPSS Inc., Chicago, IL) was used to analyze the data of descriptive statistics (mean, standard deviation, range, technical error of measure relative, absolute, and the confidence interval—CI 95%) were used to describe the sample, and correlation coefficient was applied to verify the basic assumption of the relations between dependent and independent variables. For developing the multicomponent anthropometric equation, a multivariate regression model
For performing the validation of the models we used thePRESS statistic [
Thus, a model with a high degree of predictability for excluded observations gives the value of the
Characteristics of the total sample are shown in Table
Table
Correlation matrix between independent and dependent variables in the pediatric population.
Independent  Dependent  

Wt  SkTr  SkSi  SkHab  SkTh  CiAr  CiWs  CiTh  Br 
BrKn  PHV  Age  FM 
BMC (kg)  LST (kg)  
Ht  0.84  −0.13  0.12  0.11  −0.14  0.67  0.65  0.69  0.85  0.70  0.94  0.88  0.28  0.91  0.95 
Wt  0.29  0.54  0.52  0.27  0.91  0.89  0.89  0.87  0.77  0.87  0.78  0.70  0.92  0.91  
SkTr  0.85  0.86  0.89  0.44  0.46  0.37  0.11  0.23  −0.09  −0.17  0.82  −0.02  −0.10  
SkSi  0.90  0.80  0.65  0.68  0.54  0.34  0.38  0.17  0.08  0.92  0.24  0.17  
SkHab  0.83  0.62  0.66  0.56  0.31  0.35  0.15  0.05  0.92  0.22  0.14  
SkTh  0.41  0.45  0.38  0.09  0.19  −0.09  −0.16  0.80  −0.02  −0.11  
CiRa  0.89  0.86  0.77  0.67  0.74  0.66  0.75  0.77  0.76  
CiWa  0.83  0.75  0.69  0.70  0.61  0.78  0.74  0.72  
CiTh  0.76  0.71  0.74  0.65  0.69  0.79  0.77  
BrEl  0.79  0.82  0.75  0.46  0.84  0.87  
BrKn  0.67  0.60  0.50  0.73  0.72  
PHV  0.97  0.32  0.93  0.95  
Age  0.22  0.87  0.89 
Ht: height; Wt: weight; Sk: skinfold; SkTr: triceps; SkSi: suprailiac; SkHab: horizontal abdominal; SkTh: midthigh; Ci: circumference; CiRa: relaxed arm; CiWa: waist; CiTh: proximal thigh; Br: breadth; BrEl: elbow; BrKn: knee; PHV: years for peak height velocity; FM: fat mass, BMC: bone mineral content; LST: lean softtissue.
A centered distribution of the residuals (differences) was observed for the response components (Figure
Multivariate distribution of residuals for fat mass (FM), bone mineral content (BMC), and lean soft tissue (LST).
From all 32 initial variables used as predictors of the dependent variables, a stepwise regression was performed individually for FM, BMC, and LST in order to select the common variables for all three components, with the higher significance level. The number of predictor variables was reduced after 27 eliminations, and a final model was obtained with five independent variables and high precision (
Multicomponent anthropometric model matrix, precision, and internal crossvalidity for simultaneously measuring of body composition in boys.





Height (cm)  −0.0857  0.0032  0.0820 
Weight (kg)  0.3139  0.0392  0.6419 
SkSi (mm)  0.1970  −0.0095  −0.1964 
SkHab (mm)  0.2350  −0.0105  −0.2321 
PHV (yr)  −0.6571  0.0525  0.7047 
Precision  

0.9808  0.9930  0.9981 
Adj 
0.9805  0.9929  0.9981 

1.6660  0.1923  1.7480 
Crossvalidation  
PRESS  1162.433  15.37255  1280.083 

0.9490  0.9402  0.9804 

0.0850  0.0098  0.0892 
From the multivariate parameters, it was possible to predict simultaneously each body component (FM, BMC, and LST), considering the interrelationship of dependent variables, unlike the traditional methods (onedimensional analysis). Multicollinearity within the final independent variables was tested, and cases were found in which the variables were highly collinear. In those cases, an independent variable in the model was eliminated and performed the ratio between the largest and the lowest eigenvalues [
Table
Mean and standard deviation of DXA dependent variables by age group.
Age (years)  FM  BMC  LST 

8 ( 
6.1 ± 4.4  1.2 ± 0.2  22.0 ± 2.7 
9 ( 
6.6 ± 4.2  1.2 ± 0.2  23.3 ± 3.0 
10 ( 
7.0 ± 4.5  1.3 ± 0.2  24.5 ± 3.3 
11 ( 
7.8 ± 6.2  1.5 ± 0.3*  26.5 ± 3.8* 
12 ( 
8.4 ± 6.5  1.8 ± 0.3  32.3 ± 5.3 
13 ( 
10.9 ± 9.5  2.0 ± 0.4*  36.2 ± 6.3* 
14 ( 
10.3 ± 9.4  2.3 ± 0.5*  42.4 ± 7.1* 
15 ( 
11.1 ± 8.1  2.7 ± 0.5  48.4 ± 6.8 
16 ( 
10.3 ± 5.5  3.0 ± 0.4  50.6 ± 5.0 
17 ( 
11.9 ± 8.7  3.0 ± 0.4  52.0 ± 6.0 
18 ( 
10.1 ± 8.7  3.1 ± 0.8  53.8 ± 5.9 
DXA: dualenergy Xray absorptiometry; FM: fat mass; BMC: bone mineral content; LST: lean soft tissue. *Subsequent age significantly different at
The correlations between the predicted values (of the model) and those observed (by DXA) in FM, BMC, and LST (Figure
Scatterplot of predicted and actual fat mass (FM), bone mineral content (BMC), and lean soft tissue (LST) values in the male pediatric population.
The PRESS related statistics (
In this study, the error was determined by the outcome of
Then, the final model for each dependent variable could be expressed as
The multicomponent model approach presented in this study showed a high correlation in most comparisons between independent and dependent variables (Table
The multicomponent determination of body composition during growth finds application in field and clinical settings allowing specific definition for the component of interest. In sports, for example, monitoring the training process to reduce FM or increase lean mass may be of interest to technicians, aiming to improve sports performance. For most cases, the uncertainty of which component has contributed to an increase in body weight may compromise an adequate decision for exercise prescription, since the true relationships between FM and FFM are not known. Therefore, an accurate and precise body composition estimation is required using simple methods [
In the present study, the greater associations of FM were observed with skinfolds, BMC, with growth components (height, weight, breadth, and PVC) and LST with growth components and circumferences (Table
However, the robustness of the model can be compromised if there is multicollinearity between independent variables. The multicollinearity was examined, given the natural relationships between the independent variables. Therefore, the elimination of independent variables was required, and those who are not commonly used in the literature or without a high predictive significance were removed. Apart from being a practical model, the least number of possible variables should be considered. In this case, the estimates of regression coefficients become very sensitive to small changes in the planning matrix. The variations of the estimators are high, making testing of
So far, only the FM has been predicted by pediatric anthropometric models, determined by anthropometricbased models which have been developed against densitometric techniques in children [
The method of internal validity adopted [
To facilitate a better understanding of the practical utility of the model, we show the following example for predicting FM, BMC, and LST in a 13year old boy (Table
A worked example for predicting fat mass (FM), bone mineral content (BMC), and lean soft tissue (LST) for a boy.
Variables  Measures  FM  Product  BMC  Product  LST  Product 

Height (cm) 







Weight (kg) 







Skinfolds (mm)  
Sk suprailiac 







Sk horiz. abdom 







Maturation (years)  
PHV 







 
Total (kg)  Sum (FM) = 9.28  Sum (BMC) = 1.57  Sum (LST) = 28.40 
FM: fat mass, BMC: bone mineral content; LST: lean soft tissue; Sk: skinfold; PHV: age for peak height velocity. Against original values measured by DXA (FM = 9.30; BMC = 1.50; LST = 28.50).
The products of each measure, multiplied by its
A limitation of this study is that although DXA was used as a reference method to develop our model, this technique is not considered the gold standard for pediatric populations. A fourcompartment model (4C model) is actually the most strong model for accurately assesses body composition in children as it accounts for the variability of the main FFM components [
Concluding, new anthropometricbased model for assessing body composition of children and adolescent males was proposed. Considering the unavailability of sophisticated instruments in field and clinical settings, these models proved to be a valid and alternative solution to estimate body composition in a male pediatric population.
The authors declare no conflict of interests. See the online ICMJE Conflict of Interests Forms for this paper.
The paper was reviewed on January 15, 2013 by Benjamin Gardner, BA English, Park University ’07. The authors are thankful for the support by the National Council of Technological and Scientific Development (CNPq).